1. Working in different contexts: RStudio Projects
2. Dynamic document generation: RMarkdown
3. Version control: Git + GitHub
4. Environment management: renv
5. Containerization: Docker
6. Where should I start?
7. Start collaborating

• Peikert, A., & Brandmaier, A. M. (2020). A Reproducible Data Analysis Workflow with R Markdown, Git, Make, and Docker. https://doi.org/10.31234/osf.io/8xzqy

• What it does:
  • Allows to work in multiple different contexts (projects), e.g. one for each experiment
  • Each project is own working directory, workspace, history, and source documents
  • Each project is associated with a folder on your computer (= working directory)
• Why it helps:
  • Have a separate, shareable working environment for each experiment
  • Keep all the files associated with a project together — data, scripts, results, figures
  • Work on multiple projects at once, each associated with its packages (and package versions), loaded data, etc.
  • Use only relative paths
  • Necessary basis for version control

• In RStudio: File > New Project > …

• Relative paths: path separator characters vary across systems & anchor points differ depending on contexts
  • Use the here-package (Müller, 2020) to define relative paths within the project: read.csv(here::here("data", "file_I_want.csv"))

• What it does:
  • Creates dynamic documents with embedded chunks of code (R, python, Julia, stan, …), computed results , written text etc. (= LaTeX)
  • Markdown-files can be exported to documents (docx, rtf), presentations, pdfs, websites (html), … e.g using the knitr (Xie, 2015, 2020) and tinytex (Xie, 2015, 2020; for pdfs)
  • R code is dynamically rendered, and can be given in separate chunks (’’‘{r}’’‘) or inline (’ r … ’)
• Why it helps:
  • Simple language (\(\neq\) LaTeX)
  • Integrates directly with statistical software (R Studio)
  • Saves code and output in one file
  • Reduces copy&paste errors: reported results consistent with actual results

• Installation: install.packages("rmarkdown")

• Installation: install.packages("rmarkdown")
• Open a markdown file: File > New File > R Markdown

• Installation: install.packages("rmarkdown")
• Open a markdown file: File > New File > R Markdown

• You don’t have RStudio installed: install Pandoc (http://pandoc.org) before installing markdown ()
• Lengthy R code chunks: Install knitr-package (Xie, 2014, 2015, 2020) to customize chunks and knitting process
  • {r cache=TRUE,message=FALSE,warning=FALSE,results="hide", error = TRUE}
  • or use opts_chunk$set()-function
• Knit to pdf: You need a LaTeX-installation
  • TinyTeX (Xie, 2010) is a light-weight, cross-platform distribution (install.packages("tinytex"); tinytex::install_tinytex()))
  • Separate code chunks by a blank line

• What it does:
  • Tracks changes to files (data and code) over time: Sequence of “snapshots” (commits) wit unique identifiers (hash code) and describition (commit message)
  • Allows to “go back in time”: Recall older versions or to revert the entire project
  • Changes between commits can be compared
  • Organized in repositories: Collection of all snapshots
  • GitHub: Popular server for sharing materials (privately or publicly) and collaborating via git (also: GitLab and others)

• Why it helps:
  • Keep things organized and track changes
  • Clean up code
  • Language agnostic
  • (Remote) backup
  • Work together (even simultaneously and parallely: branches, merges, pull requests)
  • Web interface to track issues
  • Easily connected e.g. to osf.io

• …

• Github: No long-term guarantee for availability of service (is commercial)
  • Mirror snapshots on hu servers/osf/zenodo/FigShare/…

• What it does:
  • Creates a project-specific library of packages in the project folder
  • Overwrites install.packages() to install packages there
  • Keeps track of package versions in the renv.lock file
• Why it helps:
  • Keeps package versions untouched by other projects
  • Allows you to revert to the previous state when an update has broken your analysis
  • Makes it easier to share package versions with your collaborators (e.g., via GitHub)

1. Install the renv package: install.packages(renv)
2. Initialize your project library: renv::init()
3. Install and update your packages as usual: install.packages("pkgname")
4. Save the current state of the project library: renv::snapshot()
5. Revert to a previous state if an update caused problems: renv::restore()

You can skip step #2 if you select “Use renv with this project” during project creation.

• If necessary

• What it does:
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• Why it helps:
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• If necessary

• Suggested order of steps, minimal and maximal version

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